library(tidyverse) # Colección de paquetes para ciencia de datos (incluye ggplot2, dplyr, tidyr, readr, purrr, tibble)
library(reshape) # Herramientas para reorganizar datos
library(Hmisc) # resúmenes estadísticos
library(limma) # Análisis de datos de expresión genética
library(AnnotationDbi) # Interfaz para bases de datos de anotaciones bioinformáticas
library(org.Hs.eg.db) # Datos de anotación para genes humanos
library(VennDiagram) # Generación de diagramas de Venn 
library(gridExtra) # Mostrar varias gráficas
library(patchwork) # Combinar múltiples ggplots en un único plot
library(ggrepel) # Mejora la visualización de texto en ggplots evitando solapamientos de texto
library(Rtsne) # Implementación de t-SNE
library(umap) # Implementación de UMAP
library(ggVennDiagram) # diagrama de Venn
library(fastDummies)  # Para realizar el one-hot encoding
library(reactable) # para tablas interactivas
library(factoextra)

1 Leyendo datos

Descargar los datos. Escalo los datos de la matriz para tener homogeneidad en las representaciones.

# covariables
ROSMAP_RINPMIAGESEX_covs <- readRDS("~/Library/CloudStorage/OneDrive-UNIVERSIDADDEMURCIA/Documentos/Fernando/Master Bioinformatica/TFM/datos/ROSMAP_RINPMIAGESEX_covs.rds")
covs <- ROSMAP_RINPMIAGESEX_covs
rownames(covs) <- covs$mrna_id
covs$study <- as.factor(covs$study)
covs$projid <- as.character(covs$projid)
covs$ceradsc <- as.factor(covs$ceradsc)
covs$cogdx <- as.factor(covs$cogdx)
covs$neuroStatus <- as.factor(covs$neuroStatus)

#datos corregidos 
ROSMAP_RINPMIAGESEX_resids <- readRDS("~/Library/CloudStorage/OneDrive-UNIVERSIDADDEMURCIA/Documentos/Fernando/Master Bioinformatica/TFM/datos/ROSMAP_RINPMIAGESEX_resids.rds")
mat_exp <- scale(ROSMAP_RINPMIAGESEX_resids)

One-hot encoding de la matriz de covariables

covs2 <- data.frame(matrix(ncol = 0, nrow = nrow(covs))) # Hago un dataframe vacio para meter los datos procesados

for (colname in names(covs)) {
  if (is.factor(covs[[colname]]) & length(levels(covs[[colname]])) > 2) {

    dummy_df <- dummy_cols(covs[colname],
                           remove_selected_columns = TRUE) # quitar las variables iniciales
    
    dummy_df <- data.frame(lapply(dummy_df, factor))

    covs2 <- cbind(covs2, dummy_df)

  } else {

    covs2[[colname]] <- covs[[colname]] # si son numéricas o de otra clase, las añadimos igual
  }
}
rownames(covs2) <- covs2$mrna_id
covs2

2 Geneset de alzheimer KEGG

2.1 Mecanismos de filtrado

Filtrar la matriz Mnxm a Mnxm’, con m’ << n para filtrar los predictores, en este caso de la matriz de expresión mat_exp.

# geneset de Alzheimer extraidos de KEGG

tab <- getGeneKEGGLinks(species="hsa")
tab$Symbol <- mapIds(org.Hs.eg.db, tab$GeneID,
                       column="SYMBOL", keytype="ENTREZID")

paths <- getKEGGPathwayNames(species="hsa")
geneset_alz <- tab$Symbol[tab$PathwayID=="hsa05010"]

mat_exp_alz_genes <- mat_exp[, colnames(mat_exp) %in% geneset_alz]

Visualizamos si se han seleccionado todos los genes y cuantos del geneset de KEGG. Nuestra matriz tiene 305 genes de un total 384 del geneset.

venn.plot <- venn.diagram(
  x = list(GenesMatriz = colnames(mat_exp), GenesetAlzheimer = geneset_alz),
  category.names = c("Matrix Genes", "Geneset Alzheimer"),
  filename = NULL,
  output = FALSE,  # Asegura que no se exporte a un archivo
  fill = c("#440154ff", '#fde725ff'),
  cex = 1,  # Aumenta el tamaño del texto
  fontface = "bold",
  cat.cex = 1,  # Aumenta el tamaño del texto de las categorías
  cat.fontface = "bold",
  cat.default.pos = "text",
  cat.pos = 25, #posicion de las categoricas
  cat.dist = 0.1, #distancia de las categoricas
  rotation.degree = 0, 
  margin = 0.1, # hacerla más pequeña
  lwd=0.5,
  lty = "dashed", # Estilo de línea discontinua
  edge.col = "grey", # Color de los bordes
  main = "Alzheimer genes in exp matrix",
  main.fontface= "bold", 
  main.cex = 2,
  main.pos = c(0.5, 1)
)

grid.newpage()  # Asegura que el lienzo esté limpio
grid.draw(venn.plot)

2.2 Mecanismo de detección de outliers

2.2.1 PCA

pca_KEGG <- prcomp(mat_exp_alz_genes)

# Scree plot con los datos escalados
var_exp <- pca_KEGG$sdev^2
prop_var_exp <- var_exp / sum(var_exp)
cum_var_exp <- cumsum(prop_var_exp)

df_var_exp <- data.frame(Comp = 1:length(prop_var_exp), VarExp = prop_var_exp)
df_cum_var_exp <- data.frame(Comp = 1:length(cum_var_exp), CumVarExp = cum_var_exp)

ggplot(df_var_exp[1:20,], aes(x = Comp, y = VarExp)) +
  geom_bar(stat = "identity", fill = "skyblue") +
  geom_line(aes(group = 1), color = "blue") +
  geom_point(color = "blue") +
  theme_minimal() +
  labs(x = "Principal components", y = "Variance", title = "Scree Plot scaled") +
  ylim(c(0,1)) +
  geom_line(data = df_cum_var_exp[1:20,], aes(x = Comp, y = CumVarExp), color="#8B1A1A") +
  geom_point(data = df_cum_var_exp[1:20,], aes(x = Comp, y = CumVarExp), color = "red") +
  geom_bar(data = df_cum_var_exp[1:20,], aes(x = Comp, y = CumVarExp), stat = "identity", fill = "red", alpha= 0.25) +
  annotate("text", x = 4, y = 0.85, label = "Cumulative Scree Plot", color = "#8B1A1A", size = 4) +
  geom_text(data = df_cum_var_exp[seq(0,20,2),], aes(x=Comp, y = CumVarExp +0.04, label = round(CumVarExp, 2)))

2.2.1.1 Seleccionar muestras extremas para las dos primeras PC

Selecciono los outliers de las dos primeras PC con el doble de SD.

outlierspc1 <- as.data.frame(pca_KEGG$x[abs(pca_KEGG$x[,1]) > 2*(pca_KEGG$sdev[1]),1])

outlierspc2 <- as.data.frame(pca_KEGG$x[abs(pca_KEGG$x[,2]) > 2*(pca_KEGG$sdev[2]),2])

df <- as.data.frame(pca_KEGG$x)

plot1 <- ggplot(df, aes(x = PC1)) +
  geom_density(fill = "#00CED1", alpha = 0.5) +
  geom_vline(xintercept = mean(df$PC1) + 2*pca_KEGG$sdev[1], linetype = "dashed", color = "blue") +
  geom_vline(xintercept = mean(df$PC1) - 2*pca_KEGG$sdev[1], linetype = "dashed", color = "blue") +
  labs(title = "PC1 density with 2*SD scaled PCA") +
  geom_text(data = outlierspc1, 
            aes(x = outlierspc1[,1], y= 0, label = rownames(outlierspc1)), 
            vjust = 1.5, hjust = 0, size = 3, color = "#E9965A", angle = 90, fontface= "bold") +
  geom_rug(data = as.data.frame(pca_KEGG$x), aes(x= PC1, y = 0), color= ifelse(abs(pca_KEGG$x[,1]) > 2*(pca_KEGG$sdev[1]), "#8B1A1A", "grey")) +
  theme_minimal()

plot2 <- ggplot(df, aes(x = PC2)) +
  geom_density(fill = "#00CED1", alpha = 0.5) +
  geom_vline(xintercept = mean(df$PC2) + 2*pca_KEGG$sdev[2], linetype = "dashed", color = "blue") +
  geom_vline(xintercept = mean(df$PC2) - 2*pca_KEGG$sdev[2], linetype = "dashed", color = "blue") +
  labs(title = "PC2 density with 2*SD scaled PCA") +
  geom_text(data = outlierspc2, 
            aes(x = outlierspc2[,1], y= 0, label = rownames(outlierspc2)), 
            vjust = 1.5, hjust = 0, size = 3, color = "#E9965A", angle = 90, fontface= "bold") +
  geom_rug(data = as.data.frame(pca_KEGG$x), aes(x= PC2, y = 0), color= ifelse(abs(pca_KEGG$x[,2]) > 2*(pca_KEGG$sdev[2]), "#8B1A1A", "grey")) +
  theme_minimal()

grid.arrange(plot1, plot2, ncol = 1)

mPC1.pos <- rownames(outlierspc1[outlierspc1[, 1] > 0 , , drop = F])
mPC1.neg <- rownames(outlierspc1[outlierspc1[, 1] < 0 , , drop = F ])

mPC2.pos <- rownames(outlierspc2[outlierspc2[, 1] > 0 , , drop = F ])
mPC2.neg <- rownames(outlierspc2[outlierspc2[, 1] < 0 , , drop = F])

# Asegurarse de que todos son vectores del mismo largo para el dataframe final
max_length <- max(length(mPC1.pos),
                  length(mPC1.neg),
                  length(mPC2.pos), 
                  length(mPC2.neg))

# Normalizar la longitud de los vectores (en caso de que alguno sea más corto)
rownames_mPC1.pos <- c(mPC1.pos, rep("", max_length - length(mPC1.pos)))
rownames_mPC1.neg <- c(mPC1.neg, rep("", max_length - length(mPC1.neg)))
rownames_mPC2.pos <- c(mPC2.pos, rep("", max_length - length(mPC2.pos)))
rownames_mPC2.neg <- c(mPC2.neg, rep("", max_length - length(mPC2.neg)))


final_table <- data.frame(
  mPC1.pos = rownames_mPC1.pos,
  mPC1.neg = rownames_mPC1.neg,
  mPC2.pos = rownames_mPC2.pos,
  mPC2.neg = rownames_mPC2.neg
)

final_table

2.2.2 tSNE

set.seed(1234)
tsne <- Rtsne(mat_exp_alz_genes, dims = 2, theta = 0.0)

tsne.data <- as.data.frame(tsne$Y)
row.names(tsne.data) <- row.names(mat_exp_alz_genes)
tsne.data.covs <- merge(tsne.data, covs, by = "row.names")
tsne.data.covs$Row.names <- NULL
row.names(tsne.data.covs) <- tsne.data.covs$mrna_id

2.2.2.1 Seleccionamos outliers

rownames(tsne.data) <- rownames(mat_exp_alz_genes)

# Calcular la media y la desviación estándar para cada componente de t-SNE
mean.tsne1 <- mean(tsne.data[,1])
sd.tsne1 <- sd(tsne.data[,1])
mean.tsne2 <- mean(tsne.data[,2])
sd.tsne2 <- sd(tsne.data[,2])

# Identificar muestras a más de 2 desviaciones estándar de la media
outliers.tsne1 <- tsne.data[abs(tsne.data[,1] - mean.tsne1) > 2 * sd.tsne1, ]
outliers.tsne2 <-tsne.data[abs(tsne.data[,2] - mean.tsne2) > 2 * sd.tsne2, ]

mtSNE1.pos <- rownames(outliers.tsne1[outliers.tsne1[, 1] > 0, , drop = F])

mtSNE1.neg <- rownames(outliers.tsne1[outliers.tsne1[, 1] < 0, , drop = F])

mtSNE2.pos <- rownames(outliers.tsne2[outliers.tsne2[, 1] > 0, , drop = F])

mtSNE2.neg <- rownames(outliers.tsne2[outliers.tsne2[, 1] < 0, , drop = F])

# Asegurarse de que todos son vectores del mismo largo para el dataframe final
max_length <- max(length(mtSNE1.pos),
                  length(mtSNE1.neg),
                  length(mtSNE2.pos), 
                  length(mtSNE2.neg))

# Normalizar la longitud de los vectores (en caso de que alguno sea más corto)
rownames_mtSNE1.pos <- c(mtSNE1.pos, rep("", max_length - length(mtSNE1.pos)))
rownames_mtSNE1.neg <- c(mtSNE1.neg, rep("", max_length - length(mtSNE1.neg)))
rownames_mtSNE2.pos <- c(mtSNE2.pos, rep("", max_length - length(mtSNE2.pos)))
rownames_mtSNE2.neg <- c(mtSNE2.neg, rep("", max_length - length(mtSNE2.neg)))


final_table <- data.frame(
  mtSNE1.pos = rownames_mtSNE1.pos,
  mtSNE1.neg = rownames_mtSNE1.neg,
  mtSNE2.pos = rownames_mtSNE2.pos,
  mtSNE2.neg = rownames_mtSNE2.neg
)

final_table

2.2.3 UMAP

local.config <- umap.defaults
# local.config$n_neighbors <- 4
# local.config$n_components <- 2
# local.config$n_epochs <- 100
# local.config$metric<- "euclidean"
set.seed(1234)
umap.ad <- umap(mat_exp_alz_genes,random_stage=1234, local.config)

umap.data <- as.data.frame(umap.ad$layout)
row.names(umap.data) <- row.names(mat_exp_alz_genes)
umap.data.covs <- merge(umap.data, covs, by = "row.names")
umap.data.covs$Row.names <- NULL
row.names(umap.data.covs) <- umap.data.covs$mrna_id

2.2.3.1 Seleccionamos outliers

rownames(umap.data) <- rownames(mat_exp_alz_genes)

# Calcular la media y la desviación estándar para cada componente de t-SNE
mean.umap1 <- mean(umap.data[,1])
sd.umap1 <- sd(umap.data[,1])
mean.umap2 <- mean(umap.data[,2])
sd.umap2 <- sd(umap.data[,2])

# Identificar muestras a más de 2 desviaciones estándar de la media
outliers.umap1 <- umap.data[abs(umap.data[,1] - mean.umap1) > 2 * sd.umap1, ]
outliers.umap2 <-umap.data[abs(umap.data[,2] - mean.umap2) > 2 * sd.umap2, ]

mUMAP1.pos <- rownames(outliers.umap1[outliers.umap1[,1] > 0 , , drop = F])

mUMAP1.neg <- rownames(outliers.umap1[outliers.umap1[,1] < 0 , , drop = F])

mUMAP2.pos <- rownames(outliers.umap2[outliers.umap2[,1] > 0 , , drop = F])

mUMAP2.neg <- rownames(outliers.umap2[outliers.umap2[,1] < 0 , , drop = F])

# Asegurarse de que todos son vectores del mismo largo para el dataframe final
max_length <- max(length(mUMAP1.pos),
                  length(mUMAP1.neg),
                  length(mUMAP2.pos), 
                  length(mUMAP2.neg))

# Normalizar la longitud de los vectores (en caso de que alguno sea más corto)
rownames_mtSNE1.pos <- c(mUMAP1.pos, rep("", max_length - length(mUMAP1.pos)))
rownames_mtSNE1.neg <- c(mUMAP1.neg, rep("", max_length - length(mUMAP1.neg)))
rownames_mtSNE2.pos <- c(mUMAP2.pos, rep("", max_length - length(mUMAP2.pos)))
rownames_mtSNE2.neg <- c(mUMAP2.neg, rep("", max_length - length(mUMAP2.neg)))


final_table <- data.frame(
  mUMAP1.pos = rownames_mtSNE1.pos,
  mUMAP1.neg = rownames_mtSNE1.neg,
  mUMAP2.pos = rownames_mtSNE2.pos,
  mUMAP2.neg = rownames_mtSNE2.neg
)

final_table

2.3 Mecanismo de anotación basado en diferencias

2.3.1 Covariables en outliers

# PCA

for (i in rownames(covs2)){
  if (i %in% mPC1.pos){
    covs2[i, "sampleset_PCA"] <- "mPC1 positive"
  }
  else if (i %in% mPC1.neg){
    covs2[i, "sampleset_PCA"] <- "mPC1 negative"
  }
  else if (i %in% mPC2.pos ){
    covs2[i, "sampleset_PCA"] <- "mPC2 positive"
  }
  else if (i %in% mPC2.neg){
    covs2[i, "sampleset_PCA"] <- "mPC2 negative"
  }
  else {
    covs2[i, "sampleset_PCA"] <- "Not in both"
  }
}

covs2$sampleset_PCA <- as.factor(covs2$sampleset_PCA)
data.frame(table(covs2$sampleset_PCA))

data.frame(table(covs2$sampleset_PCA[covs2$neuroStatus == 1]))

#tsne

for (i in rownames(covs2)){
  if (i %in% mtSNE1.pos){
    covs2[i, "sampleset_tSNE"] <- "mtSNE1 positive"
  }
  else if (i %in% mtSNE1.neg){
    covs2[i, "sampleset_tSNE"] <- "mtSNE1 negative"
  }
  else if (i %in% mtSNE2.pos){
    covs2[i, "sampleset_tSNE"] <- "mtSNE2 positive"
  }
  else if (i %in% mtSNE2.neg){
    covs2[i, "sampleset_tSNE"] <- "mtSNE2 negative"
  } else {
    covs2[i, "sampleset_tSNE"] <- "Not in both"
  }
}

covs2$sampleset_tSNE <- as.factor(covs2$sampleset_tSNE)
data.frame(table(covs2$sampleset_tSNE))

data.frame(table(covs2$sampleset_tSNE[covs2$neuroStatus == 1]))

# UMAP

for (i in rownames(covs2)){
  if (i %in% mUMAP1.pos){
    covs2[i, "sampleset_UMAP"] <- "mUMAP1 positive"
  }
  else if (i %in% mUMAP1.neg){
    covs2[i, "sampleset_UMAP"] <- "mUMAP1 negative"
  }
  else if (i %in% mUMAP2.pos){
    covs2[i, "sampleset_UMAP"] <- "mUMAP2 positive"
  }
  else if (i %in% mUMAP2.neg){
    covs2[i, "sampleset_UMAP"] <- "mUMAP2 negative"
  } else {
    covs2[i, "sampleset_UMAP"] <- "Not in both"
  }
}

covs2$sampleset_UMAP <- as.factor(covs2$sampleset_UMAP)
data.frame(table(covs2$sampleset_UMAP))

data.frame(table(covs2$sampleset_UMAP[covs2$neuroStatus == 1]))

2.3.2 Fitrado de casos

covs2.casos <- covs2 %>%
  filter(neuroStatus == 1)

2.4 Mecanismo de anotación basado en diferencias

Voy a comparar en una tabla los ratios de las covariables de los outliers comparando con los ratios de covariables de los no outliers

calculo.ratios <- function(covs2.casos){
  ratios <- data.frame()
  ratios$ratios <- numeric(0)
  
  for (i in names(covs2.casos)) {
    if (class(covs2[[i]]) == "factor") {
      
      if (grepl("sampleset_", i) | grepl("batch", i)){
        next
        
      } else {
        frecuencia <- table(covs2.casos[[i]])
  
        if (length(frecuencia) == 2) {
          ratio <- frecuencia[2] / frecuencia[1]
          nuevafila <- data.frame(ratios = ratio)
          row.names(nuevafila) <- i
          ratios <- rbind(ratios, nuevafila)
          
        } 
      } 
    } 
  }
  
  return(ratios)
}

data.frame.ratios <- function(df, sampleset) {
  niveles.sampleset <- levels(df[[sampleset]])

  ratios <- data.frame(matrix(ncol = 0, nrow = length(rownames(calculo.ratios(covs2)))))
  
  for (i in niveles.sampleset) {
    ratio <- df[df[[sampleset]] == i, ] %>%
      calculo.ratios()
    
    nombre.filas <- rownames(ratio)
    
    colnames(ratio)[1] <- i
    
    ratios <- cbind(ratios, ratio)
    rownames(ratios) <- nombre.filas  
  }
  return(ratios)
}

2.4.1 PCA

ratios.PCA.kegg <- data.frame.ratios(covs2.casos, "sampleset_PCA")

ratio.ouliers.vs.no.ouliers <- data.frame(matrix(ncol = 0, nrow = length(rownames(ratios.PCA.kegg))))

for (i in colnames(ratios.PCA.kegg)) {
  if (i == "Not in both") {
    next
    
  } else {
    ratio <- data.frame(log2(ratios.PCA.kegg[[i]] / ratios.PCA.kegg[["Not in both"]]))
    
    nombre.filas <- rownames(ratios.PCA.kegg)
    
    colnames(ratio)[1] <- paste(i, " vs No outliers")
    
    ratio.ouliers.vs.no.ouliers <- cbind(ratio.ouliers.vs.no.ouliers, ratio)
    rownames(ratio.ouliers.vs.no.ouliers) <- nombre.filas  
  }
}
ratio.ouliers.vs.no.ouliers <- round(ratio.ouliers.vs.no.ouliers, 2)
ratio.ouliers.vs.no.ouliers <- ratio.ouliers.vs.no.ouliers %>%
  filter(row_number() != which(rownames(ratio.ouliers.vs.no.ouliers) == "neuroStatus"))
ratio.ouliers.vs.no.ouliers <- replace(ratio.ouliers.vs.no.ouliers, is.na(ratio.ouliers.vs.no.ouliers), "sin casos")

reactable(ratio.ouliers.vs.no.ouliers)

reactable(data.frame(t(ratio.ouliers.vs.no.ouliers)))

2.4.2 tSNE

Solamente hay un caso por lo que no es factible hacerlo.

ratios.tsne.kegg <- data.frame.ratios(covs2.casos, "sampleset_tSNE")

2.4.3 UMAP

ratios.umap.kegg <- data.frame.ratios(covs2.casos, "sampleset_UMAP")

ratio.ouliers.vs.no.ouliers <- data.frame(matrix(ncol = 0, nrow = length(rownames(ratios.umap.kegg))))

for (i in colnames(ratios.umap.kegg)) {
  if (i == "Not in both") {
    next
    
  } else {
    ratio <- data.frame(log2(ratios.umap.kegg[[i]] / ratios.umap.kegg[["Not in both"]]))
    
    nombre.filas <- rownames(ratios.umap.kegg)
    
    colnames(ratio)[1] <- paste(i, " vs No outliers")
    
    ratio.ouliers.vs.no.ouliers <- cbind(ratio.ouliers.vs.no.ouliers, ratio)
    rownames(ratio.ouliers.vs.no.ouliers) <- nombre.filas  
  }
}

ratio.ouliers.vs.no.ouliers <- round(ratio.ouliers.vs.no.ouliers, 2)
ratio.ouliers.vs.no.ouliers <- ratio.ouliers.vs.no.ouliers %>%
  filter(row_number() != which(rownames(ratio.ouliers.vs.no.ouliers) == "neuroStatus"))

ratio.ouliers.vs.no.ouliers <- replace(ratio.ouliers.vs.no.ouliers, is.na(ratio.ouliers.vs.no.ouliers), "sin casos")

reactable(ratio.ouliers.vs.no.ouliers)

reactable(data.frame(t(ratio.ouliers.vs.no.ouliers)))

2.5 Mecanismo de anotación dependiente del mecanismo de detección

2.5.1 PCA

pca_rotation <- round(data.frame(pca_KEGG$rotation[,1:2]),2)

reactable(pca_rotation)

loadings <- pca_KEGG$rotation

loading.pc1.pos <- loadings[loadings[, "PC1"] > 0, "PC1"]
loading.pc1.neg <- loadings[loadings[, "PC1"] < 0, "PC1"]
loading.pc2.pos <- loadings[loadings[, "PC2"] > 0, "PC2"]
loading.pc2.neg <- loadings[loadings[, "PC2"] < 0, "PC2"]

best.loadings.pc1.pos <- data.frame(loading = head(sort(loading.pc1.pos, decreasing = TRUE), 5))
best.loadings.pc1.neg <- data.frame(loading =head(sort(loading.pc1.neg, decreasing = FALSE), 5), pc = "mPC1 negative")
best.loadings.pc2.pos <- data.frame(loading =head(sort(loading.pc2.pos, decreasing = TRUE), 5), pc = "mPC2 positive")
best.loadings.pc2.neg <- data.frame(loading =head(sort(loading.pc2.neg, decreasing = FALSE), 5), pc = "mPC2 negative")

pca.var <- get_pca_var(pca_KEGG)
pca.var.coord <- data.frame(pca.var$coord[,1:2])

pca.var.coord.best.loadings <-  pca.var.coord[(rownames(pca.var.coord) %in% rownames(best.loadings.pc1.pos)) | (rownames(pca.var.coord) %in% rownames(best.loadings.pc1.neg)) | (rownames(pca.var.coord) %in% rownames(best.loadings.pc2.pos)) | (rownames(pca.var.coord) %in% rownames(best.loadings.pc2.neg)), ]


pca.var.coord.best.loadings <- pca.var.coord.best.loadings %>%
  mutate(PC = case_when(
    row.names(.) %in% rownames(best.loadings.pc1.pos) ~ "mPC1 positive",
    row.names(.) %in% rownames(best.loadings.pc1.neg) ~ "mPC1 negative",
    row.names(.) %in% rownames(best.loadings.pc2.pos) ~ "mPC2 positive",
    row.names(.) %in% rownames(best.loadings.pc2.neg) ~ "mPC2 negative",
    TRUE ~ NA_character_  # para cualquier otra condición no especificada
  ))

pca.data.kegg <- data.frame(pca_KEGG$x)[,1:2]
colnames(pca.data.kegg) <- c("PC1.KEGG", "PC2.KEGG")

covs2 <- merge(covs2, pca.data.kegg, by="row.names")
rownames(covs2) <- covs2$mrna_id
covs2$Row.names <- NULL

pca.data.kegg <- data.frame(pca_KEGG$x)[,1:2]
colnames(pca.data.kegg) <- c("PC1.KEGG", "PC2.KEGG")

covs2_df <- covs2.casos%>%
  dplyr::select(mrna_id, sampleset_PCA)
para.plot <- merge(pca.data.kegg, covs2_df, by = "row.names")
rownames(para.plot) <- para.plot$Row.names
para.plot$Row.names <- NULL

pca.var.coord.best.loadings$id <- rownames(pca.var.coord.best.loadings)

p <- ggplot(para.plot, aes_string(x = "PC1.KEGG", y = "PC2.KEGG", color = "sampleset_PCA")) +
    geom_point(show.legend = TRUE, size = 3) +
    geom_text_repel(aes(label=ifelse(mrna_id %in% rownames(outlierspc1) | mrna_id %in% rownames(outlierspc2),
                                     as.character(gsub("_.*", "", covs2$mrna_id)), "")),
                  color = "black",
                  max.overlaps = 30, # Reduce el número máximo de solapamientos
                  point.padding = unit(0.2, "lines"), # Menos padding alrededor de los puntos
                  size = 3, 
                  fontface = "bold",
                  segment.size = 0.2, # Líneas de guía más finas
                  segment.color = 'grey50',
                  max.segment.length = unit(0.5, "lines"), # Líneas de guía más cortas
                  arrow = arrow(length = unit(0.02, "npc"), type = "closed", ends = "last")) +
    theme_minimal() +
    labs(title = "PCA plot loadings",
         x = "PC1.KEGG", y = "PC2.KEGG") +
    theme(
      legend.title = element_blank(),
      legend.text = element_text(size = 12),
      text = element_text(size = 12),
      legend.key.size = unit(0.5, "cm"),
      plot.margin = margin(5, 5, 5, 5)) +
  geom_point(data= (covs2 %>% filter(neuroStatus == 0)), aes_string(x = "PC1.KEGG", y = "PC2.KEGG", color = "sampleset_PCA"), alpha = 0.5)+ 
  geom_segment(data = pca.var.coord.best.loadings, aes(x= 0, y = 0, xend = Dim.1*20, yend = Dim.2*20, color = PC), arrow = arrow(length = unit(0.05, "inches"), type = "closed"),
               size = 0.4) +
  geom_text_repel(data = pca.var.coord.best.loadings,
                    aes(x = Dim.1*22, 
                        y = Dim.2*22,
                        label=ifelse((id %in% rownames(best.loadings.pc1.pos) | id %in% rownames(best.loadings.pc1.neg) | id %in% rownames(best.loadings.pc2.pos) | id %in% rownames(best.loadings.pc2.neg)), id, ""),
                        color = PC),
                  max.overlaps = 10, # Reduce el número máximo de solapamientos
                  size = 3, 
                  fontface = "bold",
                  segment.color = 'grey50',
                  arrow = arrow(length = unit(0.02, "npc"), type = "closed", ends = "last")) 

p

3 Geneset de alzheimer Andrews

3.1 Mecanismos de filtrado

Filtrar la matriz Mnxm a Mnxm’, con m’ << n para filtrar los predictores, en este caso de la matriz de expresión mat_exp.

geneset.AD.Andrews <- read_delim("~/Library/CloudStorage/OneDrive-UNIVERSIDADDEMURCIA/Documentos/Fernando/Master Bioinformatica/TFM/datos/geneset AD Andrews 2023 Lancet.csv", delim = ";", escape_double = FALSE, trim_ws = TRUE)

geneset.AD.Andrews <- unique(geneset.AD.Andrews$Gene)

mat.expre.AD.Andrews <- data.frame(mat_exp[, colnames(mat_exp) %in% geneset.AD.Andrews])

Visualizamos si se han seleccionado todos los genes y cuantos del geneset de KEGG. Nuestra matriz tiene 305 genes de un total 384 del geneset.

venn.plot <- venn.diagram(
  x = list(GenesMatriz = colnames(mat_exp), GenesetAlzheimer = geneset.AD.Andrews),
  category.names = c("Matrix Genes", "Geneset Alzheimer"),
  filename = NULL,
  output = FALSE,  # Asegura que no se exporte a un archivo
  fill = c("#440154ff", '#fde725ff'),
  cex = 1,  # Aumenta el tamaño del texto
  fontface = "bold",
  cat.cex = 1,  # Aumenta el tamaño del texto de las categorías
  cat.fontface = "bold",
  cat.default.pos = "text",
  cat.pos = 25, #posicion de las categoricas
  cat.dist = 0.1, #distancia de las categoricas
  rotation.degree = 0, 
  margin = 0.1, # hacerla más pequeña
  lwd=0.5,
  lty = "dashed", # Estilo de línea discontinua
  edge.col = "grey", # Color de los bordes
  main = "Alzheimer genes in exp matrix",
  main.fontface= "bold", 
  main.cex = 2,
  main.pos = c(0.5, 1)
)

grid.newpage()  # Asegura que el lienzo esté limpio
grid.draw(venn.plot)

3.2 Mecanismo de detección de outliers

3.2.1 PCA

pca.AD.Andrews <- prcomp(mat.expre.AD.Andrews)

3.2.1.1 Seleccionar muestras extremas para las dos primeras PC